Geoghegan, C. | bioRxiv Science

bioRxiv2025

Immunometabolic Reprogramming of Monocytes in Tuberculosis Infection and Disease

RationaleMonocytes are central to host defence against Mycobacterium tuberculosis (Mtb), yet their functional and metabolic profiles during latent TB infection (TBI) and active TB disease (TBD) remain poorly defined. Immunometabolic dysfunction may underlie ineffective responses in TB, but cell-specific mechanisms are unclear. ObjectivesTo compare the phenotypic, functional, and metabolic profiles of circulating monocytes from individuals with TBI, TBD, and healthy controls (HC), and assess the impact of treatment. MeasurementsPeripheral blood monocytes were profiled using high-dimensional flow cytometry, Luminex cytokine/chemokine assays, and SCENITH, a flow-based metabolic assay. Unstimulated and Mtb-stimulated monocytes from treatment-naive and treated individuals were analysed. Main ResultsMonocytes from TBI and TBD showed distinct phenotypes from HC, marked by elevated CD14 and CD45RA. HLA-DR was reduced in TBI versus HC and further decreased in TBD. TNF receptors were downregulated in TBI but unchanged in TBD. Baseline cytokine and chemokine profiles in TBI and TBD were similar (yet distinct from HC), but Mtb stimulation elicited a stronger cytokine response in TBI. Metabolically, TBI and TBD monocytes exhibited increased glycolysis and reduced mitochondrial dependence versus HC. Treatment partially restored mitochondrial function. Upon Mtb challenge, TBI monocytes had higher glycolytic capacity than TBD. ConclusionsMonocyte metabolic plasticity and cytokine production distinguish latent from active TB and are partially reversible with treatment. Circulating monocyte metabolism reflects TB immune status and may serve as a biomarker or therapeutic target. Reprogrammed glycolytic profiles in TBI contrast with impaired adaptability in TBD, suggesting dysfunctional myeloid activation during disease.

immunology↗

bioRxiv2024

Castanet: a pipeline for rapid analysis of targeted multi-pathogen genomic data

MotivationTarget enrichment strategies generate genomic data from multiple pathogens in a single process, greatly improving sensitivity over metagenomic sequencing and enabling cost-effective, high throughput surveillance and clinical applications. However, uptake by research and clinical laboratories is constrained by an absence of computational tools that are specifically designed for the analysis of multi-pathogen enrichment sequence data. Here we present the Castanet pipeline: an analysis pipeline for end-to-end processing and consensus sequence generation for use with multi-pathogen enrichment sequencing data. Castanet is designed to work with short-read data produced by existing targeted enrichment strategies, but can be readily deployed on any BAM file generated by another methodology. It is packaged with usability features, including graphical interface and installer script. ResultsIn addition to genome reconstruction, Castanet reports method-specific metrics that enable quantification of capture efficiency, estimation of pathogen load, differentiation of low-level positives from contamination, and assessment of sequencing quality. Castanet can be used as a traditional end-to-end pipeline for consensus generation, but its strength lies in the ability to process a flexible, pre-defined set of pathogens of interest directly from multi-pathogen enrichment experiments. In our tests, Castanet consensus sequences were accurate reconstructions of reference sequences, including in instances where multiple strains of the same pathogen were present. Castanet performs effectively on standard laptop computers and can process the entire output of a 96-sample enrichment sequencing run (50M reads) using a single batch process command, in < 2 h. Availability and ImplementationSource code freely available under GPL-3 license at https://github.com/MultipathogenGenomics/castanet, implemented in Python 3.10 and supported in Ubuntu Linux 22.04 and other Bash-like environments. The data for this study have been deposited in the European Nucleotide Archive (ENA) at EMBL-EBI under accession number PRJEB77004.

bioinformatics↗